Overview

Dataset statistics

Number of variables15
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.7 KiB
Average record size in memory120.3 B

Variable types

Numeric13
Categorical2

Alerts

duration is highly overall correlated with profit and 7 other fieldsHigh correlation
profit is highly overall correlated with duration and 7 other fieldsHigh correlation
acq_exp is highly overall correlated with acq_exp_sqHigh correlation
ret_exp is highly overall correlated with duration and 7 other fieldsHigh correlation
acq_exp_sq is highly overall correlated with acq_expHigh correlation
ret_exp_sq is highly overall correlated with duration and 7 other fieldsHigh correlation
freq is highly overall correlated with duration and 7 other fieldsHigh correlation
freq_sq is highly overall correlated with duration and 7 other fieldsHigh correlation
crossbuy is highly overall correlated with duration and 7 other fieldsHigh correlation
sow is highly overall correlated with duration and 7 other fieldsHigh correlation
acquisition is highly overall correlated with duration and 7 other fieldsHigh correlation
customer is uniformly distributedUniform
customer has unique valuesUnique
duration has 162 (32.4%) zerosZeros
ret_exp has 162 (32.4%) zerosZeros
ret_exp_sq has 162 (32.4%) zerosZeros
freq has 162 (32.4%) zerosZeros
freq_sq has 162 (32.4%) zerosZeros
crossbuy has 162 (32.4%) zerosZeros
sow has 162 (32.4%) zerosZeros

Reproduction

Analysis started2023-04-15 19:25:05.236991
Analysis finished2023-04-15 19:25:22.093613
Duration16.86 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

customer
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.5
Minimum1
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-15T14:25:22.149366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.95
Q1125.75
median250.5
Q3375.25
95-th percentile475.05
Maximum500
Range499
Interquartile range (IQR)249.5

Descriptive statistics

Standard deviation144.48183
Coefficient of variation (CV)0.57677378
Kurtosis-1.2
Mean250.5
Median Absolute Deviation (MAD)125
Skewness0
Sum125250
Variance20875
MonotonicityStrictly increasing
2023-04-15T14:25:22.272902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
330 1
 
0.2%
343 1
 
0.2%
342 1
 
0.2%
341 1
 
0.2%
340 1
 
0.2%
339 1
 
0.2%
338 1
 
0.2%
337 1
 
0.2%
336 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
500 1
0.2%
499 1
0.2%
498 1
0.2%
497 1
0.2%
496 1
0.2%
495 1
0.2%
494 1
0.2%
493 1
0.2%
492 1
0.2%
491 1
0.2%

acquisition
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
1
338 
0
162 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 338
67.6%
0 162
32.4%

Length

2023-04-15T14:25:22.370646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-15T14:25:22.461288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 338
67.6%
0 162
32.4%

Most occurring characters

ValueCountFrequency (%)
1 338
67.6%
0 162
32.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 338
67.6%
0 162
32.4%

Most occurring scripts

ValueCountFrequency (%)
Common 500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 338
67.6%
0 162
32.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 338
67.6%
0 162
32.4%

duration
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct278
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean742.454
Minimum0
Maximum1673
Zeros162
Zeros (%)32.4%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-15T14:25:22.543072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median957.5
Q31146.25
95-th percentile1457.15
Maximum1673
Range1673
Interquartile range (IQR)1146.25

Descriptive statistics

Standard deviation544.47204
Coefficient of variation (CV)0.73334111
Kurtosis-1.3758027
Mean742.454
Median Absolute Deviation (MAD)279.5
Skewness-0.40648538
Sum371227
Variance296449.8
MonotonicityNot monotonic
2023-04-15T14:25:22.652848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 162
32.4%
950 4
 
0.8%
1122 4
 
0.8%
1207 3
 
0.6%
1050 3
 
0.6%
1006 3
 
0.6%
1039 3
 
0.6%
1252 3
 
0.6%
982 3
 
0.6%
1242 3
 
0.6%
Other values (268) 309
61.8%
ValueCountFrequency (%)
0 162
32.4%
654 1
 
0.2%
655 1
 
0.2%
658 1
 
0.2%
661 1
 
0.2%
678 1
 
0.2%
683 1
 
0.2%
687 1
 
0.2%
689 1
 
0.2%
694 1
 
0.2%
ValueCountFrequency (%)
1673 1
0.2%
1660 1
0.2%
1656 1
0.2%
1649 1
0.2%
1647 1
0.2%
1643 1
0.2%
1635 1
0.2%
1633 1
0.2%
1631 1
0.2%
1617 1
0.2%

profit
Real number (ℝ)

Distinct498
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2403.8428
Minimum-1027.04
Maximum6134.3
Zeros0
Zeros (%)0.0%
Negative162
Negative (%)32.4%
Memory size4.0 KiB
2023-04-15T14:25:22.761282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1027.04
5-th percentile-754.0105
Q1-316.3175
median3369.925
Q33931.5975
95-th percentile4785.397
Maximum6134.3
Range7161.34
Interquartile range (IQR)4247.915

Descriptive statistics

Standard deviation2083.1912
Coefficient of variation (CV)0.86660875
Kurtosis-1.3727848
Mean2403.8428
Median Absolute Deviation (MAD)785.16
Skewness-0.54320082
Sum1201921.4
Variance4339685.7
MonotonicityNot monotonic
2023-04-15T14:25:22.862802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3605.86 2
 
0.4%
3718.4 2
 
0.4%
6134.3 1
 
0.2%
3535 1
 
0.2%
3487.88 1
 
0.2%
-852.25 1
 
0.2%
-244 1
 
0.2%
4084.57 1
 
0.2%
3388.2 1
 
0.2%
-503.88 1
 
0.2%
Other values (488) 488
97.6%
ValueCountFrequency (%)
-1027.04 1
0.2%
-1016.18 1
0.2%
-970.31 1
0.2%
-902.47 1
0.2%
-891.97 1
0.2%
-868.21 1
0.2%
-852.25 1
0.2%
-851.77 1
0.2%
-838.33 1
0.2%
-837.83 1
0.2%
ValueCountFrequency (%)
6134.3 1
0.2%
5655.11 1
0.2%
5652.77 1
0.2%
5569.71 1
0.2%
5559.1 1
0.2%
5446.32 1
0.2%
5444.28 1
0.2%
5435.51 1
0.2%
5433.94 1
0.2%
5338.39 1
0.2%

acq_exp
Real number (ℝ)

Distinct497
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean493.351
Minimum1.21
Maximum1027.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-15T14:25:22.975335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.21
5-th percentile220.64
Q1384.1375
median491.66
Q3600.205
95-th percentile774.9025
Maximum1027.04
Range1025.83
Interquartile range (IQR)216.0675

Descriptive statistics

Standard deviation166.948
Coefficient of variation (CV)0.33839599
Kurtosis0.022706289
Mean493.351
Median Absolute Deviation (MAD)108.525
Skewness0.12858104
Sum246675.5
Variance27871.634
MonotonicityNot monotonic
2023-04-15T14:25:23.081850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
482.33 2
 
0.4%
492 2
 
0.4%
387.23 2
 
0.4%
693.54 1
 
0.2%
429.09 1
 
0.2%
244 1
 
0.2%
504.18 1
 
0.2%
582.78 1
 
0.2%
503.88 1
 
0.2%
627.99 1
 
0.2%
Other values (487) 487
97.4%
ValueCountFrequency (%)
1.21 1
0.2%
43.15 1
0.2%
120.86 1
0.2%
121.18 1
0.2%
136.26 1
0.2%
147.12 1
0.2%
151.04 1
0.2%
165.98 1
0.2%
167.09 1
0.2%
167.99 1
0.2%
ValueCountFrequency (%)
1027.04 1
0.2%
1016.18 1
0.2%
970.31 1
0.2%
902.47 1
0.2%
891.97 1
0.2%
868.21 1
0.2%
864.1 1
0.2%
852.25 1
0.2%
851.77 1
0.2%
838.33 1
0.2%

ret_exp
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct339
Distinct (%)67.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean336.26198
Minimum0
Maximum1094.96
Zeros162
Zeros (%)32.4%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-15T14:25:23.191463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median398.095
Q3514.2625
95-th percentile747.588
Maximum1094.96
Range1094.96
Interquartile range (IQR)514.2625

Descriptive statistics

Standard deviation266.5893
Coefficient of variation (CV)0.79280239
Kurtosis-0.7538814
Mean336.26198
Median Absolute Deviation (MAD)165.7
Skewness0.094315153
Sum168130.99
Variance71069.855
MonotonicityNot monotonic
2023-04-15T14:25:23.401780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 162
32.4%
971.56 1
 
0.2%
340.15 1
 
0.2%
499.59 1
 
0.2%
491.75 1
 
0.2%
539.03 1
 
0.2%
316.05 1
 
0.2%
308.53 1
 
0.2%
978.52 1
 
0.2%
350.7 1
 
0.2%
Other values (329) 329
65.8%
ValueCountFrequency (%)
0 162
32.4%
229.87 1
 
0.2%
231.41 1
 
0.2%
234.06 1
 
0.2%
235.84 1
 
0.2%
237.12 1
 
0.2%
238.77 1
 
0.2%
246.75 1
 
0.2%
252 1
 
0.2%
262.45 1
 
0.2%
ValueCountFrequency (%)
1094.96 1
0.2%
1082.37 1
0.2%
1001.8 1
0.2%
996.86 1
0.2%
983.56 1
0.2%
978.52 1
0.2%
971.56 1
0.2%
965.4 1
0.2%
953.49 1
0.2%
925.11 1
0.2%

acq_exp_sq
Real number (ℝ)

Distinct497
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271211.1
Minimum1.46
Maximum1054811.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-15T14:25:23.509490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.46
5-th percentile48682.038
Q1147561.95
median241729.67
Q3360246.04
95-th percentile600474.91
Maximum1054811.2
Range1054809.7
Interquartile range (IQR)212684.09

Descriptive statistics

Standard deviation172829.25
Coefficient of variation (CV)0.63724992
Kurtosis1.6916648
Mean271211.1
Median Absolute Deviation (MAD)100210.58
Skewness1.1059475
Sum1.3560555 × 108
Variance2.986995 × 1010
MonotonicityNot monotonic
2023-04-15T14:25:23.626529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
232642.23 2
 
0.4%
242064 2
 
0.4%
149947.07 2
 
0.4%
480997.73 1
 
0.2%
184118.23 1
 
0.2%
59536 1
 
0.2%
254197.47 1
 
0.2%
339632.53 1
 
0.2%
253895.05 1
 
0.2%
394371.44 1
 
0.2%
Other values (487) 487
97.4%
ValueCountFrequency (%)
1.46 1
0.2%
1861.92 1
0.2%
14607.14 1
0.2%
14684.59 1
0.2%
18566.79 1
0.2%
21644.29 1
0.2%
22813.08 1
0.2%
27549.36 1
0.2%
27919.07 1
0.2%
28220.64 1
0.2%
ValueCountFrequency (%)
1054811.16 1
0.2%
1032621.79 1
0.2%
941501.5 1
0.2%
814452.1 1
0.2%
795610.48 1
0.2%
753788.6 1
0.2%
746668.81 1
0.2%
726330.06 1
0.2%
725512.13 1
0.2%
702797.19 1
0.2%

ret_exp_sq
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct339
Distinct (%)67.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183999.83
Minimum0
Maximum1198937.4
Zeros162
Zeros (%)32.4%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-15T14:25:23.735059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median158479.67
Q3264466.08
95-th percentile558894.61
Maximum1198937.4
Range1198937.4
Interquartile range (IQR)264466.08

Descriptive statistics

Standard deviation201982.6
Coefficient of variation (CV)1.0977325
Kurtosis4.7905071
Mean183999.83
Median Absolute Deviation (MAD)155460.96
Skewness1.8455725
Sum91999917
Variance4.0796969 × 1010
MonotonicityNot monotonic
2023-04-15T14:25:23.854681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 162
32.4%
943928.83 1
 
0.2%
115702.02 1
 
0.2%
249590.17 1
 
0.2%
241818.06 1
 
0.2%
290553.34 1
 
0.2%
99887.6 1
 
0.2%
95190.76 1
 
0.2%
957501.39 1
 
0.2%
122990.49 1
 
0.2%
Other values (329) 329
65.8%
ValueCountFrequency (%)
0 162
32.4%
52840.22 1
 
0.2%
53550.59 1
 
0.2%
54784.08 1
 
0.2%
55620.51 1
 
0.2%
56225.89 1
 
0.2%
57011.11 1
 
0.2%
60885.56 1
 
0.2%
63504 1
 
0.2%
68880 1
 
0.2%
ValueCountFrequency (%)
1198937.4 1
0.2%
1171524.82 1
0.2%
1003603.24 1
0.2%
993729.86 1
0.2%
967390.27 1
0.2%
957501.39 1
0.2%
943928.83 1
0.2%
931997.16 1
0.2%
909143.18 1
0.2%
855828.51 1
0.2%

freq
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.22
Minimum0
Maximum21
Zeros162
Zeros (%)32.4%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-15T14:25:23.957476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q311
95-th percentile15
Maximum21
Range21
Interquartile range (IQR)11

Descriptive statistics

Standard deviation5.5339723
Coefficient of variation (CV)0.88970616
Kurtosis-0.96785092
Mean6.22
Median Absolute Deviation (MAD)6
Skewness0.34422912
Sum3110
Variance30.62485
MonotonicityNot monotonic
2023-04-15T14:25:24.040237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 162
32.4%
9 37
 
7.4%
11 36
 
7.2%
10 28
 
5.6%
6 27
 
5.4%
5 27
 
5.4%
8 25
 
5.0%
12 23
 
4.6%
7 22
 
4.4%
13 17
 
3.4%
Other values (12) 96
19.2%
ValueCountFrequency (%)
0 162
32.4%
1 12
 
2.4%
2 9
 
1.8%
3 8
 
1.6%
4 14
 
2.8%
5 27
 
5.4%
6 27
 
5.4%
7 22
 
4.4%
8 25
 
5.0%
9 37
 
7.4%
ValueCountFrequency (%)
21 2
 
0.4%
20 2
 
0.4%
19 3
 
0.6%
18 6
 
1.2%
17 1
 
0.2%
16 10
2.0%
15 13
2.6%
14 16
3.2%
13 17
3.4%
12 23
4.6%

freq_sq
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.252
Minimum0
Maximum441
Zeros162
Zeros (%)32.4%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-15T14:25:24.152025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median36
Q3121
95-th percentile225
Maximum441
Range441
Interquartile range (IQR)121

Descriptive statistics

Standard deviation84.551134
Coefficient of variation (CV)1.2209197
Kurtosis2.6412152
Mean69.252
Median Absolute Deviation (MAD)36
Skewness1.5680946
Sum34626
Variance7148.8943
MonotonicityNot monotonic
2023-04-15T14:25:24.247470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 162
32.4%
81 37
 
7.4%
121 36
 
7.2%
100 28
 
5.6%
36 27
 
5.4%
25 27
 
5.4%
64 25
 
5.0%
144 23
 
4.6%
49 22
 
4.4%
169 17
 
3.4%
Other values (12) 96
19.2%
ValueCountFrequency (%)
0 162
32.4%
1 12
 
2.4%
4 9
 
1.8%
9 8
 
1.6%
16 14
 
2.8%
25 27
 
5.4%
36 27
 
5.4%
49 22
 
4.4%
64 25
 
5.0%
81 37
 
7.4%
ValueCountFrequency (%)
441 2
 
0.4%
400 2
 
0.4%
361 3
 
0.6%
324 6
 
1.2%
289 1
 
0.2%
256 10
2.0%
225 13
2.6%
196 16
3.2%
169 17
3.4%
144 23
4.6%

crossbuy
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.052
Minimum0
Maximum11
Zeros162
Zeros (%)32.4%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-15T14:25:24.348999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q37
95-th percentile9
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.2419618
Coefficient of variation (CV)0.8000893
Kurtosis-1.313598
Mean4.052
Median Absolute Deviation (MAD)2.5
Skewness-0.034735021
Sum2026
Variance10.510317
MonotonicityNot monotonic
2023-04-15T14:25:24.427890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 162
32.4%
5 64
 
12.8%
6 61
 
12.2%
7 57
 
11.4%
8 47
 
9.4%
4 43
 
8.6%
3 25
 
5.0%
9 19
 
3.8%
10 9
 
1.8%
1 5
 
1.0%
Other values (2) 8
 
1.6%
ValueCountFrequency (%)
0 162
32.4%
1 5
 
1.0%
2 4
 
0.8%
3 25
 
5.0%
4 43
 
8.6%
5 64
 
12.8%
6 61
 
12.2%
7 57
 
11.4%
8 47
 
9.4%
9 19
 
3.8%
ValueCountFrequency (%)
11 4
 
0.8%
10 9
 
1.8%
9 19
 
3.8%
8 47
9.4%
7 57
11.4%
6 61
12.2%
5 64
12.8%
4 43
8.6%
3 25
 
5.0%
2 4
 
0.8%

sow
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct89
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.88
Minimum0
Maximum116
Zeros162
Zeros (%)32.4%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-15T14:25:24.523350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median44
Q366
95-th percentile85
Maximum116
Range116
Interquartile range (IQR)66

Descriptive statistics

Standard deviation31.805294
Coefficient of variation (CV)0.81803741
Kurtosis-1.3051346
Mean38.88
Median Absolute Deviation (MAD)28
Skewness0.016694844
Sum19440
Variance1011.5768
MonotonicityNot monotonic
2023-04-15T14:25:24.646926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 162
32.4%
66 12
 
2.4%
54 11
 
2.2%
40 9
 
1.8%
80 9
 
1.8%
67 8
 
1.6%
76 8
 
1.6%
70 8
 
1.6%
62 8
 
1.6%
68 8
 
1.6%
Other values (79) 257
51.4%
ValueCountFrequency (%)
0 162
32.4%
1 2
 
0.4%
5 2
 
0.4%
7 1
 
0.2%
8 1
 
0.2%
9 1
 
0.2%
12 2
 
0.4%
13 1
 
0.2%
18 1
 
0.2%
22 3
 
0.6%
ValueCountFrequency (%)
116 1
 
0.2%
115 1
 
0.2%
107 1
 
0.2%
104 1
 
0.2%
101 1
 
0.2%
100 3
0.6%
99 1
 
0.2%
98 1
 
0.2%
95 2
0.4%
94 2
0.4%

industry
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
1
261 
0
239 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 261
52.2%
0 239
47.8%

Length

2023-04-15T14:25:24.754446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-15T14:25:24.838173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 261
52.2%
0 239
47.8%

Most occurring characters

ValueCountFrequency (%)
1 261
52.2%
0 239
47.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 261
52.2%
0 239
47.8%

Most occurring scripts

ValueCountFrequency (%)
Common 500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 261
52.2%
0 239
47.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 261
52.2%
0 239
47.8%

revenue
Real number (ℝ)

Distinct469
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.54008
Minimum14.49
Maximum65.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-15T14:25:24.924790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum14.49
5-th percentile23.9705
Q133.53
median41.43
Q347.515
95-th percentile55.4925
Maximum65.1
Range50.61
Interquartile range (IQR)13.985

Descriptive statistics

Standard deviation9.6724441
Coefficient of variation (CV)0.23858967
Kurtosis-0.36419077
Mean40.54008
Median Absolute Deviation (MAD)6.835
Skewness-0.11270265
Sum20270.04
Variance93.556176
MonotonicityNot monotonic
2023-04-15T14:25:25.042328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44.31 3
 
0.6%
47.2 2
 
0.4%
41.55 2
 
0.4%
42.05 2
 
0.4%
52.2 2
 
0.4%
38.86 2
 
0.4%
46.64 2
 
0.4%
50.14 2
 
0.4%
41.32 2
 
0.4%
36.55 2
 
0.4%
Other values (459) 479
95.8%
ValueCountFrequency (%)
14.49 1
0.2%
15.86 1
0.2%
16.29 1
0.2%
17.74 1
0.2%
19.15 1
0.2%
19.4 1
0.2%
19.51 1
0.2%
19.94 1
0.2%
20.15 1
0.2%
20.3 1
0.2%
ValueCountFrequency (%)
65.1 1
0.2%
65.03 1
0.2%
64.48 1
0.2%
63.17 1
0.2%
62.47 1
0.2%
61.24 1
0.2%
60.96 1
0.2%
60.71 1
0.2%
60 1
0.2%
59.7 1
0.2%

employees
Real number (ℝ)

Distinct383
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean671.532
Minimum18
Maximum1461
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-15T14:25:25.154884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile309.95
Q1503
median657.5
Q3826
95-th percentile1105.35
Maximum1461
Range1443
Interquartile range (IQR)323

Descriptive statistics

Standard deviation242.9914
Coefficient of variation (CV)0.36184634
Kurtosis0.05821867
Mean671.532
Median Absolute Deviation (MAD)162.5
Skewness0.22914002
Sum335766
Variance59044.819
MonotonicityNot monotonic
2023-04-15T14:25:25.271396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
667 5
 
1.0%
423 4
 
0.8%
589 4
 
0.8%
934 3
 
0.6%
767 3
 
0.6%
896 3
 
0.6%
455 3
 
0.6%
937 3
 
0.6%
818 3
 
0.6%
711 3
 
0.6%
Other values (373) 466
93.2%
ValueCountFrequency (%)
18 1
0.2%
24 1
0.2%
56 1
0.2%
104 1
0.2%
143 1
0.2%
161 1
0.2%
165 1
0.2%
171 1
0.2%
176 1
0.2%
177 1
0.2%
ValueCountFrequency (%)
1461 1
0.2%
1423 1
0.2%
1361 1
0.2%
1354 1
0.2%
1337 1
0.2%
1259 1
0.2%
1253 1
0.2%
1203 1
0.2%
1180 1
0.2%
1174 1
0.2%

Interactions

2023-04-15T14:25:20.584990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:06.471570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:07.587154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:08.719966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:09.868135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:11.089705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:12.229164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:13.396538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:14.563298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:15.797852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:17.040489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:18.188268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:19.358321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:20.671847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:06.550313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:07.673842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:08.811520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:09.947941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:11.172250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:12.317821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:13.483495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:14.644071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:15.879381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:17.119894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:18.272801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:19.444835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:20.766603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:06.636446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:07.765425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:08.904824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:10.031740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:11.262557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:12.418351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:13.577267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:14.735561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:15.977106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:17.211210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:18.365318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:19.535344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:20.862302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:06.725904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:07.851766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:09.007341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:10.122507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:11.348264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:12.519869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:13.671999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:14.826075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:16.065629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:17.297215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:18.460401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:19.624606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:20.949156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:06.810703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:07.932549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:09.088369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:10.200291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:11.432788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:12.610386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:13.763618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:14.917587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:16.173157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:17.394736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:18.551641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:19.715785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:21.035917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:06.910177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:08.021933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:09.172920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:10.278302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:11.509328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:12.703902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:13.868135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:14.995782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:16.284689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:17.480935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:18.644283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:19.797709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:21.127625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:06.997589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:08.105986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:09.256621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:10.472134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:11.592923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:12.793910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:13.958168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:15.079285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:16.381202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:17.571458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:18.734896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:19.886313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:21.215674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:07.083770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:08.198397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:09.342408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:10.560200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:11.681530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:12.888348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:14.049864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:15.267673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:16.481260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:17.661262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:18.832691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:19.985880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:21.295481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:07.163309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:08.285874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:09.424433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:10.638958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:11.763155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:12.970996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:14.128648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:15.341282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:16.555847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:17.742984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:18.910370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:20.062396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:21.376673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:07.252012image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:08.369386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:09.508443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:10.716732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:11.852604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:13.054583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:14.213254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:15.423803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:16.655369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:17.826710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:18.997595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:20.142227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:21.468855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:07.331612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:08.457627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:09.596040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:10.797795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:11.937483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:13.137167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:14.298342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:15.530441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:16.762739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:17.916155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:19.088527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:20.223744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:21.561000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:07.416238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:08.547387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:09.686209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:10.898958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:12.032005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:13.220620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:14.389969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:15.633843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:16.865345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:18.018662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:19.186094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:20.410675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:21.646772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:07.499084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:08.628173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:09.780824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:10.986072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:12.134637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:13.302422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:14.469682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:15.706848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:16.946958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:18.102368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:19.265639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-15T14:25:20.493315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-15T14:25:25.387850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
customerdurationprofitacq_expret_expacq_exp_sqret_exp_sqfreqfreq_sqcrossbuysowrevenueemployeesacquisitionindustry
customer1.0000.0260.024-0.0320.027-0.0320.0270.0400.0400.0710.0110.0160.0270.0000.000
duration0.0261.0000.9290.0360.9900.0360.9900.6460.6460.7140.6920.1780.3880.9940.241
profit0.0240.9291.0000.0990.9280.0990.9280.6630.6630.7240.7020.1940.4390.9940.236
acq_exp-0.0320.0360.0991.0000.0281.0000.0280.0260.0260.0420.0580.071-0.0100.2990.000
ret_exp0.0270.9900.9280.0281.0000.0281.0000.6990.6990.7100.6790.1790.4080.9930.225
acq_exp_sq-0.0320.0360.0991.0000.0281.0000.0280.0260.0260.0420.0580.071-0.0100.2820.000
ret_exp_sq0.0270.9900.9280.0281.0000.0281.0000.6990.6990.7100.6790.1790.4080.8200.246
freq0.0400.6460.6630.0260.6990.0260.6991.0001.0000.6910.6770.1700.4440.9020.229
freq_sq0.0400.6460.6630.0260.6990.0260.6991.0001.0000.6910.6770.1700.4440.6550.086
crossbuy0.0710.7140.7240.0420.7100.0420.7100.6910.6911.0000.6920.1770.4260.9690.228
sow0.0110.6920.7020.0580.6790.0580.6790.6770.6770.6921.0000.2200.4010.9600.245
revenue0.0160.1780.1940.0710.1790.0710.1790.1700.1700.1770.2201.0000.0550.2190.000
employees0.0270.3880.439-0.0100.408-0.0100.4080.4440.4440.4260.4010.0551.0000.5000.000
acquisition0.0000.9940.9940.2990.9930.2820.8200.9020.6550.9690.9600.2190.5001.0000.236
industry0.0000.2410.2360.0000.2250.0000.2460.2290.0860.2280.2450.0000.0000.2361.000

Missing values

2023-04-15T14:25:21.788697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-15T14:25:21.998125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customeracquisitiondurationprofitacq_expret_expacq_exp_sqret_exp_sqfreqfreq_sqcrossbuysowindustryrevenueemployees
01116356134.30693.54971.56480997.73943928.83636595147.20898
12110393523.62460.03449.53211627.60202077.2211121622045.11686
23112884080.62249.03805.0462015.94648089.4021441690029.101423
3400-638.47638.470.00407643.940.000000040.64181
45116315446.32588.98919.84346897.44846105.6324980048.72631
5619423488.07568.65365.11323362.82133305.31749448135.43617
6717992704.89373.15341.42139240.92116567.6215225551050.96911
78110813577.98366.49509.22134314.92259305.0113169523139.53645
8900-284.96284.960.0081202.200.000000029.33530
91000-292.54292.540.0085579.650.000000142.13511
customeracquisitiondurationprofitacq_expret_expacq_exp_sqret_exp_sqfreqfreq_sqcrossbuysowindustryrevenueemployees
49049100-268.42268.420.0072049.300.000000149.83468
49149200-364.59364.590.00132925.870.000000039.76511
492493110683440.18345.52454.24119384.07206333.9824740155.25629
493494110364111.34592.71573.45351305.14328844.9020400628149.90622
494495110963247.85270.48488.2673159.43238397.8311121667135.811461
495496111013519.55281.80506.7379411.24256775.2911121846143.38818
496497116435079.82482.33953.49232642.23909143.1810100561023.10554
49749819753240.56350.76403.23123032.58162594.43864863141.35772
49849900-473.63473.630.00224325.380.000000043.81509
49950017643552.25731.41285.47534960.5981493.12981968125.481057